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Data Compression Conference (DCC)

作者： Takehiro Moriya Yutaka Kamamoto Noboru Harada NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation Japan

ISBN: (纸本)9781424464258;9780769539942

Motivated by the rapid increase of VoIP services with G.711 for telephone speech, a new ITU-T recommendation, G.711.0 (frame-wise stateless lossless compression scheme for G.711 log PCM symbols), has been standardized. The standard scheme has several coding parts, each of which is adaptively selected depending on the characteristics of the input. Among them, the mapped domain prediction part is the one most frequently activated for normal speech signals. This part consists of linear prediction in the mapped domain and variable length coding of the prediction residual. It is useful for log-compressed/expanded signal, such as ITU-T G.711. This paper describes three newly devised enhancement tools for the coding of prediction residual signals: progressive order prediction, quantized prediction order, and adaptive and sub-frame base coding for separation parameters. The design criterion is the maximization of the averaged FoM (figure of merit) over frame lengths of 40, 80, 160, 240, and 320 samples. The first tool, progressive order prediction associated with the adaptive modification of the separation parameter for the first and second samples, enhances the compression ratio by 0.5 % with a negligible increase of the complexity. The second tool, quantized prediction order, improves the compression ratio by 0.2 % with even reduced complexity. The third tool, sub-frame base adaptive coding of separation parameters, gives a 0.2 % improvement in the compression ratio with comparable complexity. All three schemes are consistently and independently effective for improving the compression ratio, although the amount of improvement with each tool is small. At the same time, none of the tools have any significant impact on computational complexity. Therefore, all the devised tools improve the FoM and have been adopted in the mapped domain prediction part of the ITU-T G.711.0 standard.

关键词： linear predictive coding Internet telephony Data compression Laboratories Telegraphy Speech enhancement Speech coding Phase change materials Adaptive coding Computational complexity

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A low-bit rate segment vocoder using minimum residual energy criteria

A low-bit rate segment vocoder using minimum residual energy...

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National Conference on Communications (NCC)

作者： Abhijit Pradhan Sadhana Chevireddy Kamakoti Veezhinathan Hema Murthy Department of Computer Science Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9781424463831;9781424463855

In speech coding, segment vocoders offer good intelligibility at low bit rates. A segment vocoder has four basic components 1) Segmentation of input speech 2) Segment quantization 3) Residual quantization 4) Synthesis of speech. Most segment vocoders use a recognition approach to segment quantization. In this paper, we assume a different approach to segment quantization. The segmental unit is a syllable and the segment codebook stores the sequence of LPC vectors. During the encoding process the speech segment is quantized using the sequence of LPC vectors that result in the smallest residual energy. PESQ scores indicate that this vocoder achieves better quality compared to that of a corresponding vocoder that uses a speech recognition framework.

关键词： Vocoders Speech synthesis linear predictive coding Encoding Bit rate Signal synthesis Hidden Markov models Quantization Decoding Speech coding

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A novel image edge detection method using linear Prediction

A novel image edge detection method using Linear Prediction

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： James Z. Zhang Peter C. Tay Robert D. Adams Department of Engineering and Technology The Kimmel School Western Carolina University Cullowhee NC USA

Traditionally, linear Prediction is used to predict future values of a signal using past values. The goal is to minimize prediction errors. In this paper, we propose a novel method of utilizing prediction errors to extract edges of images. In this method, smooth prediction errors are minimized while steep changes (larger errors) are amplified. Therefore, when applied to image edge detection, edge information can be accurately extracted. The proposed method is compared with predominant methods such as Sobel and Canny methods. While there is no mathematical proof that the proposed method outperforms predominant methods, however, examples presented in this paper may suggest that the proposed method may perform better for certain applications.

关键词： Image edge detection Data mining Nonlinear filters Computer errors linear predictive coding Equations Filtering Speech processing Feature extraction Text recognition

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Two Novel FDLP based Feature Extraction Methods for Improvement of Speech Recognition

Two Novel FDLP based Feature Extraction Methods for Improvem...

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International Symposium on Telecommunications

作者： Yasser Shekofteh Farshad AlmasGanj Ahmadreza Rezaei Mohammad Mohsen Goodarzi Biomedical Engineering Faculty Amirkabir University of Technology

ISBN: (纸本)9781424481835

In conventional automatic speech recognition systems, linguistic information of the speech signal are usually acquired from short-time frames about 10-30 ms. In this paper we have proposed two novel methods extracting the long-term information of the speech signal. Both of the methods are based on "sub-band FDLP" which divides the long-time frame of signal into several sub-bands. Using the MFCC algorithm, we are able to represent the long-term temporal features of the each sub-band. Our results show that the proposed methods could improve the recognition ratio by %1.73. The proposed methods were evaluated using the FarsDat database and the method's robustness against different conditions of noise was experimented.

关键词： Feature extraction Speech recognition linear predictive coding Time-frequency analysis

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Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes

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COMPUTERS IN BIOLOGY AND MEDICINE 2009年第9期39卷 824-843页

作者： Bahoura, Mohammed Univ Quebec Dept Engn Rimouski PQ G5L 3A1 Canada

In this paper, we present the pattern recognition methods proposed to classify respiratory sounds into normal and wheeze classes. We evaluate and compare the feature extraction techniques based on Fourier transform, linear predictive coding, wavelet transform and Mel-frequency cepstral coefficients (MFCC) in combination with the classification methods based on vector quantization, Gaussian mixture models (GMM) and artificial neural networks, using receiver operating characteristic curves. We propose the use of an optimized threshold to discriminate the wheezing class from the normal one. Also, post-processing filter is employed to considerably improve the classification accuracy. Experimental results show that our approach based on MFCC coefficients combined to GMM is well adapted to classify respiratory sounds in normal and wheeze classes. McNemar's test demonstrated significant difference between results obtained by the presented classifiers (p < 0.05). (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Respiratory sounds Wavelet transform Mel-frequency cepstral coefficients linear predictive coding Vector quantization Gaussian mixture models Multi-layer perceptron Receiver operating characteristic Statistical significance McNemar's test

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Data transmission over GSM voice channel using digital modulation technique based on autoregressive modeling of speech production

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DIGITAL SIGNAL PROCESSING 2009年第4期19卷 612-627页

作者： Kotnik, Bojan Mezgec, Zdenko Svecko, Janja Chowdhury, Amor Margento BV NL-1043 BW Amsterdam Netherlands Res Ctr Maribor Ultra Doo Maribor 2000 Slovenia

This paper presents a novel digital data modulation and demodulation algorithm ARDMA based on the principles of autoregressive modeling (AR) of speech production. In the first step a sustained voiced speech signal characteristics are analyzed using autoregressive modeling principle and then the two sets of linear prediction (LPC) coefficients are obtained and converted to linear spectrum frequencies (LSF). The input binary data stream drives the selection mechanism of LSF coefficients which are then applied as filter coefficients of the modulation signal synthesis filter. This filter is excited with specially designed excitation signal which corresponds to the basic characteristics of typical excitation signal of human vocal tract. Finally, a speech-alike modulation signal is produced. This modulation signal is then sent through the voice channel of the GSM system. The demodulator analyzes the incoming modulation signal using autoregressive modeling. The most likely LSF vector which modulated the particular symbol was determined by the demodulation process and converted to the respective string of binary data. The performance of proposed modulation scheme was compared to the regular frequency shift keying method (FSK). The performance improvement of ARDMA against FSK is observed at higher bit-rates in the case of three compared GSM speech coders. (c) 2008 Elsevier Inc. All rights reserved.

关键词： Autoregressive modeling linear predictive coding Digital modulations Speech production model GSM Speech codecs Data transmission systems

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APPLYING IMPROVED SPECTRAL MODELING FOR HIGH QUALITY VOICE CONVERSION

APPLYING IMPROVED SPECTRAL MODELING FOR HIGH QUALITY VOICE C...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Villavicencio, Fernando Roebel, Axel Rodet, Xavier Univ Pompeu Fabra Music Technol Grp Ocata 1 Barcelona 08003 Spain CNRS IRCAM STMS Anal Synthesis Team F-75004 Paris France

ISBN: (纸本)9781424423538

In this work, accurate spectral envelope estimation is applied to Voice Conversion in order to achieve High-Quality timbre conversion. True-Envelope based estimators allow model order selection leading to an adaptation of the spectral features to the characteristics of the speaker. Optimal residual signals can also be computed following a local adaptation of the model order in terms of the F-0. A new perceptual criteria is proposed to measure the impact of the spectral conversion error. The proposed envelope models show improved spectral conversion performance as well as increased converted-speech quality when compared to linear Prediction.

关键词： Speech synthesis speech analysis cepstral analysis spectral analysis linear predictive coding

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Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano

Support Vector Machines and MLP for automatic classification...

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19th Italian Workshop of the Italian-Society-for-Neural-Network (SIREN) on Neural Nets (WIRN)

作者： Giacco, Ferdinando Esposito, Antonietta Maria Scarpetta, Silvia Giudicepietro, Flora Marinaro, Maria Univ Salerno Dept Phys I-84081 Baronissi SA Italy Osserv Vesuviano Ist Nazl Geofis & Vulcanol Naples Italy Ist Nazl Fis Nucl Salerno Italy INFM CNISM Salerno Italy Inst Adv Sci Studies Vietri Sul Mare Italy

ISBN: (纸本)9781607500728

We applied and compared two supervised pattern recognition techniques, namely the Multilayer Perceptron (MLP) and Support Vector Machine (SVM), to classify seismic signals recorded on Stromboli volcano. The available data are firstly preprocessed in order to obtain a compact representation of the raw seismic signals. We extract from data spectral and temporal information so that each input vector is made up of 71 components, containing both spectral and temporal information extracted from the early signal. We implemented two classification strategies to discriminate three different seismic events: landslide, explosion-quake, and volcanic microtremor signals. The first method is a two-layer MLP network, with a Cross-Entropy error function and logistic activation function for the output units. The second method is a Support Vector Machine, whose multi-class setting is accomplished through a 1vsAll architecture with gaussian kernel. The experiments show that although the MLP produces very good results, the SVM accuracy is always higher, both in term of best performance, 99.5%, and average performance, 98.8%, obtained with different sampling permutations of training and test sets.

关键词： Seismic signals discrimination linear predictive coding Neural Networks Support Vector Machine Multilayer Perceptron

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Audio Watermark Detection Using Undetermined ICA

Audio Watermark Detection Using Undetermined ICA

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8th International Conference on Independent Component Analysis and Signal Separation

作者： Seok, Jongwon Malik, Hafiz Changwon Natl Univ Dept Informat & Commun Engn Chang Won Kyongnam South Korea Univ Michigan Dept Elect & Com Dearborn MI USA

ISBN: (纸本)9783642005985

This paper presents a blind watermark detection scheme for additive watermark embedding model. The proposed estimation-correlation-based watermark detector first estimates the embedded watermark by exploiting non-Gaussian of the real-world audio signal and the mutual independence between the host-signal and the embedded watermark and then a correlation-based detector is used to determine the presence or the absence of the watermark. For watermark estimation, blind Source separation (BSS) based on underdetermined independent component analysis (UICA) is used. Low watermark-to-signal ratio (WSR) is one to the limitations of blind detection for additive embedding model. The proposed detector uses two-stage processing to improve WSR at the blind detector;first stage removes the audio spectrum from the watermarked audio signal using linear predictive (LP) filtering and the second stage uses resulting residue from the LP filtering stage to estimate the embedded watermark using BSS based on UICA. Simulation results show that the proposed detector performs significantly better than existing estimation-correlation-based detection schemes.

关键词： Audio Watermark Detection Blind Source Separation Mean-Field Approaches Independent Component Analysis linear predictive coding

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AN ERROR ROBUST ULTRA LOW DELAY AUDIO CODER USING AN MA PREDICTION MODEL

AN ERROR ROBUST ULTRA LOW DELAY AUDIO CODER USING AN MA PRED...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wabnik, Stefan Schuller, Gerald Kraemer, Ferenc Fraunhofer IDMT Inst Digital Media Technol Ehrenbergstr 31 D-98693 Ilmenau Germany Tech Univ Ilmenau Inst Media Technol D- 98693 Ilmenau Germany

ISBN: (纸本)9781424423538

This paper compares two prediction structures for predictive perceptual audio coding in the context of the Ultra Low Delay (ULD) coding scheme. One structure is based on the commonly used AR signal model, leading to an IIR predictor in the decoder. The other structure is based on an MA signal model, leading to an FIR predictor in the decoder. We find that the AR-based predictor has a slightly better performance in case of an undisturbed transmission channel, but the MA-based predictor has a much better performance in case of transmission errors. For a Bit Error Rate (BER) of 1.0e-5, the perceptual quality of the proposed MA model predictor achieves a mean Objective Difference Grade (ODG) of -0.66 ODG whereas the AR. model predictor only reaches -3.42 ODG.

关键词： Low Delay Audio coding linear predictive coding Moving average processes Autoregressive processes Robustness

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